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				<h1>Andrew Cao 曹路</h1>
				<p>
					I am a 2th year MSE student at <a href="http://english.pku.edu.cn/">Peking University</a> working with <a href="http://www.ss.pku.edu.cn/index.php/teacherteam/teacherlist/1640-%E4%BF%9E%E6%95%AC%E6%9D%BE">Jingsong Yu</a>.
					I have also worked with <a href='http://cvgl.stanford.edu/silvio/'><a href="https://itu.edu/faculty/may-huang">Dr. May Huang</a>,
					<a href="https://www.linkedin.com/in/eric-chen-a645402b/">Eric Chen</a> and <a href="https://itu.edu/spotlight/Karl-Wang">Dr. Karl L. Wang</a>
					under double-degree joint project at <a href="https://itu.edu/">International Technological University</a>.
				</p>
				<p>
					Before coming to Peking University, I have worked with <a href='http://www.hainu.edu.cn/stm/xinxi/2015115/10409855.shtml'>Zhuhua Hu</a> and Yaochi Zhao during my undergrad at Hainan University.
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					Over the summers, I've been lucky to be an intern with Kaifu Wang and Shouye Peng at TAL Education Group Intelligent R&D Department.
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				<p><i><b>Interest:</b> My recent research interests focus on Natural Language Processing, Deep Learning, Data Mining, Robot Control System and Computer Vision.</i></p>

				<i style="color: red;">I am currently looking for partners to do more projects. Please feel free to contact with me :)</i>

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				<b>News</b> &nbsp;&nbsp; <a href="https://github.com/andrewcao95/personal-profile-list/blob/master/news/news.md">more detail</a>
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			<ul>
				<li>(2018/12/01) I won the honorary title of merit student of Peking University.</li>

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				<b>Contact</b>
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					<p>Email: <a href="mailto:andrewcao95@gmail.com">andrewcao95@gmail.com</a></p>
					<p>Github: <a href="https://github.com/andrewcao95">https://github.com/andrewcao95</a></p>
					<p>Linkedin: <a href="https://www.linkedin.com/in/andrewcao95">https://www.linkedin.com/in/andrewcao95</a></p>
					<p>Kaggle: <a href="https://www.kaggle.com/andrewcao95">https://www.kaggle.com/andrewcao95</a></p>
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				<i>
					<p>Resume: <a href="files/CAOLU_CV_EN.pdf">[in English]</a>  &nbsp;&nbsp;&nbsp; <a href="files/CAOLU_CV_EN.pdf">[in Chinese]</a>  &nbsp;&nbsp;&nbsp; (10/22/2018)</p>
					<p>Leetcode: <a href="https://leetcode.com/andrewcao95">https://leetcode.com/andrewcao95</a></p>
					<p>Photograph Gallery: <a href="https://andrewcao95.tuchong.com">https://andrewcao95.tuchong.com</a></p>
					<!-- <p>Mail Address: Peking University, No.5 Yiheyuan Rd, Haidian District, Beijing, China(100871)</p> -->
					<p>Mail Address: 2711 N 1st St, San Jose, California, United States 95134</p>
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				<b>Education</b>
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			<i>
				<p>Dec. 2018 - Now, Master of Science in Computer Engineering, <a href="https://itu.edu/">International Technological University</a> (San Francisco Bay Area, California, United States)</p>
				<p>Sep. 2017 - Now, Master of Engineering in Software Engineering, <a href="http://english.pku.edu.cn/">Peking University</a> (Beijing City, China)</p>
				<p>Sep. 2013 - Jul. 2017, Bachelor of Engineering in Network Engineering, <a href="http://www.hainu.edu.cn/">Hainan University</a> (Haikou City, China)</p>
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				<b>Work Experience</b>
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				<p>Jan. 2019 - Now, Artificial Intelligence Research Lab, International Technological University (San Francisco Bay Area, California, United States), Research Assistant</p>
				<p>Jul. 2018 - Dec. 2018, Intelligent R&D Department, TAL Education Group[top1 education & technology enterprise in China] (Beijing City, China), Intern (Nature Language Processing)</p>
				<p>Jan. 2018 - Feb. 2018, Science & Technology Big Data Research Center, Tsinghua University (Beijing City, China), Intern (Data Mining)</p>
				<p>Dec. 2016 - Apr. 2017, Department of Aquaculture & Department of Network Engineering, Hainan University (Haikou City, China), Research Assistant</p>
				<p>Feb. 2016 - Jul. 2016, State Key Laboratory for the Utilization of Marine Resources in the South China Sea (Haikou City, China), Research Assistant</p>
				<p>Sep. 2015 - Jan. 2016, Network System Operation Department, Network Technology Center of Hainan University (Haikou City, China), Operations Assistant</p>
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				<b>Selected Project</b> &nbsp;&nbsp; <a href="https://github.com/andrewcao95/personal-profile-list/blob/master/projects/projects.md">see more</a>
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					<p><b>Artificial intelligence poetry robot</b></p>
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						<i><p>co-worker: Lu Cao, <a href="https://github.com/xzwj">Yuan Tian, <a href="http://iir.ruc.edu.cn/~chenj/index.html">Jun Chen</a></p></i>
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							<a src= "">[detail and code]</a>
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				<b>Selected Publication</b> &nbsp;&nbsp; <a href="https://github.com/andrewcao95/personal-profile-list/blob/master/papers/papers.md">see more</a>
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					<p><b>A Sequential Compressed Spectrum Sensing Algorithm against SSDH Attack in Cognitive Radio Networks</b></p>
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						<p><i>co-author: Zhuhua Hu, Yong Bai, Lu Cao, Mengxing Huang, Mingshan Xie (2018/11/11 - 2018/12/11)</i></p>
						<p>
							Spectrum sensing is one of the key technologies in wireless wideband communication.
							There are still challenges in respect of how to realize fast and robust wideband spectrum sensing technology.
							In this paper, a novel nonreconstructed sequential compressed wideband spectrum sensing algorithm (NSCWSS) is proposed.
							Firstly, the algorithm uses a sequential spectrum sensing method based on history memory and reputation to ensure the robustness of the algorithm.
							Secondly, the algorithm uses the strategy of compressed sensing without reconstruction, which thus ensures the sensing agility of the algorithm.
							The algorithm is simulated and analyzed by using the centralized cooperative sensing.
							The theoretical analysis and simulation results reveal that, under the condition of ensuring the certain detection probability,
							the proposed algorithm effectively reduces complex computation of signal reconstruction, significantly reducing the wideband spectrum sampling rate.
							At the same time, in the cognitive wideband communication scenarios, the algorithm also achieves a better defense against the SSDF attack in spectrum sensing.
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							<a href= "https://doi.org/10.1155/2018/4782718">[paper]</a>
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					<p><b>Fish eye recognition based on weighted constraint AdaBoost and pupil diameter automatic measurement with improved Hough circle transform</b></p>
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						<p><i>co-author: Zhuhua Hu, Yiran Zhang, Yaochi Zhao, Lu Cao, Yong Bai, Mengxing Huang</i></p>
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							In aquaculture, fisheye pupil diameters are important for the assessment of the growth of fish, which provide reference for later breeding and selection.
							Since fisheye pupil is embedded in the body of fish, it is harder to measure the diameter of fisheye pupil than measure body length, width and tail length.
							Traditional measurement of fish eye diameter in aquaculture, which is direct touching of the fish body using measuring tools,
							has low efficiency as well as high subjectivity since it is only based on manual work.
							Considering the above factors, we introduce computer vision and machine learning to the measurement of fisheye pupil diameters.
							An improved AdaBoost algorithm based on weighted constraint is proposed in this paper, which is used in fisheye classifier training;
							and an improved Hough circle transform is put forward to achieve real-time fish eye pupil diameter measurement. Firstly, in natural light conditions …
						</p>
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							<a href= "https://www.ingentaconnect.com/content/tcsae/tcsae/2017/00000033/00000023/art00029">[paper]</a>
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					<p><b>Fish eye recognition based on weighted constraint AdaBoost and pupil diameter automatic measurement with improved Hough circle transform</b></p>
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						<p><i>co-author: Zhuhua Hu, Lu Cao, Yiran Zhang, Yaochi Zhao (2017/02)</i></p>
						<p>
							In aquaculture, fisheye pupil diameters are important for the assessment of the growth of fish, which provide reference for later breeding and selection.
							Since fisheye pupil is embedded in the body of fish, it is harder to measure the diameter of fisheye pupil than measure body length, width and tail length.
							Traditional measurement of fish eye diameter in aquaculture, which is direct touching of the fish body using measuring tools,
							has low efficiency as well as high subjectivity since it is only based on manual work.
							Considering the above factors, we introduce computer vision and machine learning to the measurement of fisheye pupil diameters.
							An improved AdaBoost algorithm based on weighted constraint is proposed in this paper, which is used in fisheye classifier training;
							and an improved Hough circle transform is put forward to achieve real-time fish eye pupil diameter measurement. Firstly, in natural light conditions …
						</p>
						<p>
							<a href= "http://www.en.cnki.com.cn/Article_en/CJFDTotal-HDXY201702009.htm">[paper]</a>
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				<b>Selected Patent</b> &nbsp;&nbsp; <a href="https://github.com/andrewcao95/personal-profile-list/blob/master/patents/patents.md">see more</a>
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					<p><b>Method for automatically measuring fisheye feature (2017-07-17)</b></p>
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						<p><i>co-author: Zhuhua Hu, Yaochi Zhao, Lu Cao</i></p>
						<p>
							The invention relates to a method for automatically measuring a fisheye feature.
							The method comprises the following steps: acquiring a fish image and removing background;
							extracting a fisheye image in the fish image without the background;
							and determining a fisheye pixel size according to the fisheye image,
							and transforming the fisheye pixel size into an actual size.
							When the fish image is acquired, a fish image acquiring device requires to be constructed.
							The fish image acquiring device comprises a standard platform, a mechanical arm and an acquiring camera;
							the acquiring camera is connected with the standard platform through the mechanical arm;
							the standard platform is used for containing a fish body to be measured;
							the mechanical arm is used for adjusting the distance between the acquiring camera and the standard platform and the positions of the acquiring camera and the standard platform;
							and the acquiring camera is used for shooting the fish image.
							By the method, non-contact type automatic measurement is realized by a computer vision technology and an image processing technology,
							and the accuracy and stability of measured data are guaranteed while the measurement efficiency is greatly improved.
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							<a href= "https://patents.google.com/patent/CN107462221A/en">[link]</a>
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				<b>Collaboration</b>
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